Data Viz Overview

Data visualization is an essential part of the statistical analysis process, both for exploratory analyses and summarizing findings.

Exploratory Data Analysis

  • Used for data quality checks

  • Help explore and understand the data

  • Typically, not seen by anyone else

Polished Data Visualization

  • Used to summarize data in presentations or papers

  • Should stand alone with appropriate titles, axes, labels, and captions

Spatial Data Viz Tools

There are many tools for creating spatial figures (GIS software, Tableau, etc…), but we will exclusively use R and the wide range of packages within it.

In particular, we will use:

  • ggplot2

  • ggmap

  • leaflet

  • RgoogleMaps

  • and many others…

Point Data: What is this?

Point Data: How about now?

Point Data: Is this better?

Principles for Point Data

  1. Include useful background for appropriate context: there are several approaches for acquiring maps in R. Sometimes streets may be more useful, but in other situation a terrain image might be more relevant.
  2. With a point patterns, use transparency or heat map summaries to distinguish between areas of higher and lower intensity.
  3. Include useful titles, labels, and where appropriate, captions (all figures). These figures should stand alone.
  4. Sources should be cited in figures.

Another Example

Additional References